Moreover, in the case of balanced excitatory and inhibitory amplitudes the output firing rate tended to increase for larger input firing rates.
H ( u − θ ) denotes the output firing rate of a neuron, which means that a neuron fires at its maximum rate when the potential exceeds a threshold, and does not fire otherwise.
An important component in these studies is the sigmoidal activation function that describes the nonlinear relationship between the population's input current reflected partially in the local field potential (LFP) and its output firing rate.
One approach to create (varDelta ^{i}) is to consider the stationary phase PDF (rho^{i}) and output firing rate that would be produced if the inputs with (varDelta _{mathrm{exc}}^{i}), (varDelta _{mathrm{inh}}^{i}) were considered in isolation.
The main neural population, the excitatory and inhibitory interneurons, are in each case described by both a second-order ordinary differential operator, which transforms the mean incoming firing rate into the mean membrane potential, and a nonlinear function, which transforms the mean membrane potential into the mean output firing rate.
Before we introduce in detail the two models sketched in Fig. 2, it is worth to note that, for weak stimuli and weak independent noise, these models possess the same signal output cross-spectrum, the same power spectrum, and the same time-dependent output firing rate.
If the limit cycle lies above the threshold, the output firing rate is roughly constant.
When the limit cycle is located totally above or below the threshold, the output firing rates are all constant.
In this paper we concentrate on the mean output firing rate with respect to different input frequencies.
As a result, the output firing rate of the whole neural network is boosted by positive feedback over the output rate of an individual neuron.
A constant efferent firing rate means that no information about the temporal input frequency F is contained in the output firing rate.
